Package evaluation of ConformalPrediction on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T16:12:55.390


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 5.08s


################################################################################
# Installation
#

Installing ConformalPrediction...
   Resolving package versions...
    Updating `~/.julia/environments/v1.10/Project.toml`
  [98bfc277] + ConformalPrediction v0.1.13
    Updating `~/.julia/environments/v1.10/Manifest.toml`
  [621f4979] + AbstractFFTs v1.5.0
  [7d9f7c33] + Accessors v0.1.41
  [79e6a3ab] + Adapt v4.2.0
  [66dad0bd] + AliasTables v1.1.3
  [dce04be8] + ArgCheck v2.4.0
  [4fba245c] + ArrayInterface v7.18.0
  [a9b6321e] + Atomix v1.1.0
  [fbb218c0] + BSON v0.3.9
  [198e06fe] + BangBang v0.4.3
  [9718e550] + Baselet v0.1.1
  [fa961155] + CEnum v0.5.0
  [324d7699] + CategoricalArrays v0.10.8
  [af321ab8] + CategoricalDistributions v0.1.15
  [082447d4] + ChainRules v1.72.2
  [d360d2e6] + ChainRulesCore v1.25.1
⌅ [3da002f7] + ColorTypes v0.11.5
  [bbf7d656] + CommonSubexpressions v0.3.1
  [34da2185] + Compat v4.16.0
  [a33af91c] + CompositionsBase v0.1.2
  [ed09eef8] + ComputationalResources v0.3.2
  [98bfc277] + ConformalPrediction v0.1.13
  [187b0558] + ConstructionBase v1.5.8
  [6add18c4] + ContextVariablesX v0.1.3
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.18.20
  [e2d170a0] + DataValueInterfaces v1.0.0
  [244e2a9f] + DefineSingletons v0.1.2
  [8bb1440f] + DelimitedFiles v1.9.1
  [b429d917] + DensityInterface v0.4.0
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.15.1
  [31c24e10] + Distributions v0.25.117
  [ffbed154] + DocStringExtensions v0.9.3
  [cc61a311] + FLoops v0.2.2
  [b9860ae5] + FLoopsBase v0.1.1
  [5789e2e9] + FileIO v1.16.6
  [1a297f60] + FillArrays v1.13.0
  [6a86dc24] + FiniteDiff v2.27.0
  [53c48c17] + FixedPointNumbers v0.8.5
⌅ [587475ba] + Flux v0.14.25
  [f6369f11] + ForwardDiff v0.10.38
⌅ [d9f16b24] + Functors v0.4.12
  [0c68f7d7] + GPUArrays v11.2.2
  [46192b85] + GPUArraysCore v0.2.0
  [076d061b] + HashArrayMappedTries v0.2.0
  [34004b35] + HypergeometricFunctions v0.3.27
  [7869d1d1] + IRTools v0.4.14
  [4846b161] + InferOpt v0.6.1
  [22cec73e] + InitialValues v0.3.1
  [3587e190] + InverseFunctions v0.1.17
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.4
  [42fd0dbc] + IterativeSolvers v0.9.4
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [033835bb] + JLD2 v0.5.11
  [692b3bcd] + JLLWrappers v1.7.0
  [b14d175d] + JuliaVariables v0.2.4
  [63c18a36] + KernelAbstractions v0.9.34
  [929cbde3] + LLVM v9.2.0
  [b964fa9f] + LaTeXStrings v1.4.0
⌅ [92ad9a40] + LearnAPI v0.1.0
  [d3d80556] + LineSearches v7.3.0
  [7a12625a] + LinearMaps v3.11.4
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [c2834f40] + MLCore v1.0.0
⌃ [7e8f7934] + MLDataDevices v1.5.3
  [a7f614a8] + MLJBase v1.7.0
  [50ed68f4] + MLJEnsembles v0.4.3
⌅ [094fc8d1] + MLJFlux v0.5.1
  [6ee0df7b] + MLJLinearModels v0.10.0
  [e80e1ace] + MLJModelInterface v1.11.0
  [d8e11817] + MLStyle v0.4.17
  [f1d291b0] + MLUtils v0.4.7
  [1914dd2f] + MacroTools v0.5.15
  [dbeba491] + Metalhead v0.9.5
  [128add7d] + MicroCollections v0.2.0
  [e1d29d7a] + Missings v1.2.0
  [d41bc354] + NLSolversBase v7.8.3
  [872c559c] + NNlib v0.9.27
  [77ba4419] + NaNMath v1.1.2
  [71a1bf82] + NameResolution v0.1.5
  [0b1bfda6] + OneHotArrays v0.2.6
  [429524aa] + Optim v1.11.0
⌅ [3bd65402] + Optimisers v0.3.4
  [bac558e1] + OrderedCollections v1.8.0
  [90014a1f] + PDMats v0.11.32
  [d96e819e] + Parameters v0.12.3
  [570af359] + PartialFunctions v1.2.0
  [85a6dd25] + PositiveFactorizations v0.2.4
  [aea7be01] + PrecompileTools v1.2.1
  [21216c6a] + Preferences v1.4.3
  [8162dcfd] + PrettyPrint v0.2.0
  [08abe8d2] + PrettyTables v2.4.0
  [33c8b6b6] + ProgressLogging v0.1.4
  [92933f4c] + ProgressMeter v1.10.2
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [c1ae055f] + RealDot v0.1.0
  [3cdcf5f2] + RecipesBase v1.3.4
  [189a3867] + Reexport v1.2.2
  [42d2dcc6] + Referenceables v0.1.3
  [97f35ef4] + RequiredInterfaces v0.1.7
  [ae029012] + Requires v1.3.0
  [79098fc4] + Rmath v0.8.0
  [321657f4] + ScientificTypes v3.1.0
  [30f210dd] + ScientificTypesBase v3.0.0
  [7e506255] + ScopedValues v1.3.0
  [efcf1570] + Setfield v1.1.1
  [605ecd9f] + ShowCases v0.1.0
  [699a6c99] + SimpleTraits v0.9.4
  [a2af1166] + SortingAlgorithms v1.2.1
  [dc90abb0] + SparseInverseSubset v0.1.2
  [276daf66] + SpecialFunctions v2.5.0
  [171d559e] + SplittablesBase v0.1.15
  [90137ffa] + StaticArrays v1.9.12
  [1e83bf80] + StaticArraysCore v1.4.3
  [c062fc1d] + StatisticalMeasuresBase v0.1.2
  [64bff920] + StatisticalTraits v3.4.0
  [82ae8749] + StatsAPI v1.7.0
  [2913bbd2] + StatsBase v0.34.4
  [4c63d2b9] + StatsFuns v1.3.2
  [892a3eda] + StringManipulation v0.4.1
⌃ [09ab397b] + StructArrays v0.6.21
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.0
  [ac1d9e8a] + ThreadsX v0.1.12
  [3bb67fe8] + TranscodingStreams v0.11.3
  [28d57a85] + Transducers v0.4.84
  [3a884ed6] + UnPack v1.0.2
  [013be700] + UnsafeAtomics v0.3.0
⌅ [e88e6eb3] + Zygote v0.6.75
  [700de1a5] + ZygoteRules v0.2.7
  [dad2f222] + LLVMExtra_jll v0.0.35+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [0dad84c5] + ArgTools v1.1.1
  [56f22d72] + Artifacts
  [2a0f44e3] + Base64
  [ade2ca70] + Dates
  [8ba89e20] + Distributed
  [f43a241f] + Downloads v1.6.0
  [7b1f6079] + FileWatching
  [9fa8497b] + Future
  [b77e0a4c] + InteractiveUtils
  [4af54fe1] + LazyArtifacts
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2
  [8f399da3] + Libdl
  [37e2e46d] + LinearAlgebra
  [56ddb016] + Logging
  [d6f4376e] + Markdown
  [a63ad114] + Mmap
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.10.0
  [de0858da] + Printf
  [3fa0cd96] + REPL
  [9a3f8284] + Random
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization
  [6462fe0b] + Sockets
  [2f01184e] + SparseArrays v1.10.0
  [10745b16] + Statistics v1.10.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test
  [cf7118a7] + UUIDs
  [4ec0a83e] + Unicode
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.4.0+0
  [e37daf67] + LibGit2_jll v1.6.4+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.2+1
  [14a3606d] + MozillaCACerts_jll v2023.1.10
  [4536629a] + OpenBLAS_jll v0.3.23+4
  [05823500] + OpenLibm_jll v0.8.1+4
  [bea87d4a] + SuiteSparse_jll v7.2.1+1
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.52.0+1
  [3f19e933] + p7zip_jll v17.4.0+2
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Installation completed after 11.57s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 534.44s


################################################################################
# Testing
#

     Testing ConformalPrediction
      Status `/tmp/jl_zoPIqr/Project.toml`
  [4c88cf16] Aqua v0.8.11
  [5224ae11] CompatHelperLocal v0.1.27
  [98bfc277] ConformalPrediction v0.1.13
  [7806a523] DecisionTree v0.12.4
  [e30172f5] Documenter v1.8.1
  [f6006082] EvoTrees v0.16.9
⌅ [7acf609c] LightGBM v0.7.2
  [add582a8] MLJ v0.20.7
  [c6f25543] MLJDecisionTreeInterface v0.4.2
⌅ [094fc8d1] MLJFlux v0.5.1
  [6ee0df7b] MLJLinearModels v0.10.0
  [e80e1ace] MLJModelInterface v1.11.0
  [636a865e] NearestNeighborModels v0.2.3
  [91a5bcdd] Plots v1.40.9
  [bd7198b4] TaijaPlotting v1.3.0
  [8dfed614] Test
      Status `/tmp/jl_zoPIqr/Manifest.toml`
  [a4c015fc] ANSIColoredPrinters v0.0.1
  [da404889] ARFFFiles v1.5.0
  [621f4979] AbstractFFTs v1.5.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.41
  [79e6a3ab] Adapt v4.2.0
  [66dad0bd] AliasTables v1.1.3
  [4c88cf16] Aqua v0.8.11
  [dce04be8] ArgCheck v2.4.0
  [ec485272] ArnoldiMethod v0.4.0
  [7d9fca2a] Arpack v0.5.4
  [4fba245c] ArrayInterface v7.18.0
  [a9b6321e] Atomix v1.1.0
  [fbb218c0] BSON v0.3.9
  [198e06fe] BangBang v0.4.3
  [9718e550] Baselet v0.1.1
  [d1d4a3ce] BitFlags v0.1.9
  [fa961155] CEnum v0.5.0
  [324d7699] CategoricalArrays v0.10.8
  [af321ab8] CategoricalDistributions v0.1.15
⌅ [8e462317] CausalInference v0.18.0
  [082447d4] ChainRules v1.72.2
  [d360d2e6] ChainRulesCore v1.25.1
  [944b1d66] CodecZlib v0.7.8
  [35d6a980] ColorSchemes v3.29.0
⌅ [3da002f7] ColorTypes v0.11.5
⌃ [c3611d14] ColorVectorSpace v0.10.0
  [5ae59095] Colors v0.13.0
  [861a8166] Combinatorics v1.0.2
  [bbf7d656] CommonSubexpressions v0.3.1
  [34da2185] Compat v4.16.0
  [5224ae11] CompatHelperLocal v0.1.27
  [a33af91c] CompositionsBase v0.1.2
  [ed09eef8] ComputationalResources v0.3.2
  [f0e56b4a] ConcurrentUtilities v2.5.0
  [98bfc277] ConformalPrediction v0.1.13
  [187b0558] ConstructionBase v1.5.8
  [6add18c4] ContextVariablesX v0.1.3
  [d38c429a] Contour v0.6.3
  [2f13d31b] CounterfactualExplanations v1.4.5
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.7.0
  [864edb3b] DataStructures v0.18.20
  [e2d170a0] DataValueInterfaces v1.0.0
  [7806a523] DecisionTree v0.12.4
  [244e2a9f] DefineSingletons v0.1.2
  [8bb1440f] DelimitedFiles v1.9.1
  [b429d917] DensityInterface v0.4.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.117
  [ffbed154] DocStringExtensions v0.9.3
  [e30172f5] Documenter v1.8.1
  [792122b4] EarlyStopping v0.3.0
  [f446124b] EnergySamplers v1.0.3
  [f6006082] EvoTrees v0.16.9
  [460bff9d] ExceptionUnwrapping v0.1.11
  [411431e0] Extents v0.1.5
  [c87230d0] FFMPEG v0.4.2
  [cc61a311] FLoops v0.2.2
  [b9860ae5] FLoopsBase v0.1.1
  [33837fe5] FeatureSelection v0.2.2
  [5789e2e9] FileIO v1.16.6
  [48062228] FilePathsBase v0.9.23
  [1a297f60] FillArrays v1.13.0
  [6a86dc24] FiniteDiff v2.27.0
  [53c48c17] FixedPointNumbers v0.8.5
⌅ [587475ba] Flux v0.14.25
  [1fa38f19] Format v1.3.7
  [f6369f11] ForwardDiff v0.10.38
⌅ [d9f16b24] Functors v0.4.12
  [0c68f7d7] GPUArrays v11.2.2
  [46192b85] GPUArraysCore v0.2.0
  [28b8d3ca] GR v0.73.13
  [68eda718] GeoFormatTypes v0.4.4
  [cf35fbd7] GeoInterface v1.4.1
  [5c1252a2] GeometryBasics v0.5.5
  [d7ba0133] Git v1.3.1
  [86223c79] Graphs v1.12.0
  [42e2da0e] Grisu v1.0.2
  [cd3eb016] HTTP v1.10.15
  [076d061b] HashArrayMappedTries v0.2.0
  [34004b35] HypergeometricFunctions v0.3.27
  [b5f81e59] IOCapture v0.2.5
  [7869d1d1] IRTools v0.4.14
  [4846b161] InferOpt v0.6.1
  [d25df0c9] Inflate v0.1.5
  [22cec73e] InitialValues v0.3.1
  [842dd82b] InlineStrings v1.4.3
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.4
  [c8e1da08] IterTools v1.10.0
  [b3c1a2ee] IterationControl v0.5.4
  [42fd0dbc] IterativeSolvers v0.9.4
  [82899510] IteratorInterfaceExtensions v1.0.0
  [033835bb] JLD2 v0.5.11
  [1019f520] JLFzf v0.1.9
  [692b3bcd] JLLWrappers v1.7.0
  [682c06a0] JSON v0.21.4
  [b14d175d] JuliaVariables v0.2.4
  [63c18a36] KernelAbstractions v0.9.34
  [ec8451be] KernelFunctions v0.10.64
  [929cbde3] LLVM v9.2.0
  [8ac3fa9e] LRUCache v1.6.1
  [b964fa9f] LaTeXStrings v1.4.0
  [c52c1a26] LaplaceRedux v1.2.0
  [23fbe1c1] Latexify v0.16.6
  [a5e1c1ea] LatinHypercubeSampling v1.9.0
  [0e77f7df] LazilyInitializedFields v1.3.0
⌅ [92ad9a40] LearnAPI v0.1.0
⌅ [7acf609c] LightGBM v0.7.2
  [d3d80556] LineSearches v7.3.0
  [7a12625a] LinearMaps v3.11.4
  [70f5e60a] LinkedLists v0.1.1
  [2ab3a3ac] LogExpFunctions v0.3.29
  [aa2f6b4e] LogarithmicNumbers v1.4.0
  [e6f89c97] LoggingExtras v1.1.0
  [c2834f40] MLCore v1.0.0
⌃ [7e8f7934] MLDataDevices v1.5.3
⌅ [64a0f543] MLFlowClient v0.5.1
  [add582a8] MLJ v0.20.7
  [45f359ea] MLJBalancing v0.1.5
  [a7f614a8] MLJBase v1.7.0
  [c6f25543] MLJDecisionTreeInterface v0.4.2
  [50ed68f4] MLJEnsembles v0.4.3
  [7b7b8358] MLJFlow v0.5.0
⌅ [094fc8d1] MLJFlux v0.5.1
  [614be32b] MLJIteration v0.6.3
  [6ee0df7b] MLJLinearModels v0.10.0
  [e80e1ace] MLJModelInterface v1.11.0
  [d491faf4] MLJModels v0.17.7
  [03970b2e] MLJTuning v0.8.8
  [d8e11817] MLStyle v0.4.17
  [f1d291b0] MLUtils v0.4.7
  [1914dd2f] MacroTools v0.5.15
  [d0879d2d] MarkdownAST v0.1.2
  [739be429] MbedTLS v1.1.9
  [442fdcdd] Measures v0.3.2
  [6fafb56a] Memoization v0.2.2
  [626554b9] MetaGraphs v0.8.0
  [dbeba491] Metalhead v0.9.5
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [6f286f6a] MultivariateStats v0.10.3
  [d41bc354] NLSolversBase v7.8.3
  [872c559c] NNlib v0.9.27
  [77ba4419] NaNMath v1.1.2
  [71a1bf82] NameResolution v0.1.5
  [c020b1a1] NaturalSort v1.0.0
  [636a865e] NearestNeighborModels v0.2.3
  [b8a86587] NearestNeighbors v0.4.21
  [46757867] NetworkLayout v0.4.9
  [6fe1bfb0] OffsetArrays v1.15.0
  [0b1bfda6] OneHotArrays v0.2.6
  [8b6db2d4] OpenML v0.3.2
  [4d8831e6] OpenSSL v1.4.3
  [429524aa] Optim v1.11.0
⌅ [3bd65402] Optimisers v0.3.4
  [bac558e1] OrderedCollections v1.8.0
  [90014a1f] PDMats v0.11.32
  [65ce6f38] PackageExtensionCompat v1.0.2
  [d96e819e] Parameters v0.12.3
  [69de0a69] Parsers v2.8.1
  [570af359] PartialFunctions v1.2.0
  [b98c9c47] Pipe v1.3.0
  [ccf2f8ad] PlotThemes v3.3.0
  [995b91a9] PlotUtils v1.4.3
  [91a5bcdd] Plots v1.40.9
  [2dfb63ee] PooledArrays v1.4.3
  [85a6dd25] PositiveFactorizations v0.2.4
  [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [8162dcfd] PrettyPrint v0.2.0
  [54e16d92] PrettyPrinting v0.4.2
  [08abe8d2] PrettyTables v2.4.0
  [33c8b6b6] ProgressLogging v0.1.4
  [92933f4c] ProgressMeter v1.10.2
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [c1ae055f] RealDot v0.1.0
  [3cdcf5f2] RecipesBase v1.3.4
  [01d81517] RecipesPipeline v0.6.12
  [189a3867] Reexport v1.2.2
  [42d2dcc6] Referenceables v0.1.3
  [2792f1a3] RegistryInstances v0.1.0
  [05181044] RelocatableFolders v1.0.1
  [97f35ef4] RequiredInterfaces v0.1.7
  [ae029012] Requires v1.3.0
  [79098fc4] Rmath v0.8.0
  [321657f4] ScientificTypes v3.1.0
  [30f210dd] ScientificTypesBase v3.0.0
  [6e75b9c4] ScikitLearnBase v0.5.0
  [7e506255] ScopedValues v1.3.0
  [6c6a2e73] Scratch v1.2.1
  [91c51154] SentinelArrays v1.4.8
  [efcf1570] Setfield v1.1.1
  [605ecd9f] ShowCases v0.1.0
  [992d4aef] Showoff v1.0.3
  [777ac1f9] SimpleBufferStream v1.2.0
  [699a6c99] SimpleTraits v0.9.4
  [a2af1166] SortingAlgorithms v1.2.1
  [dc90abb0] SparseInverseSubset v0.1.2
  [276daf66] SpecialFunctions v2.5.0
  [171d559e] SplittablesBase v0.1.15
  [860ef19b] StableRNGs v1.0.2
  [90137ffa] StaticArrays v1.9.12
  [1e83bf80] StaticArraysCore v1.4.3
⌅ [a19d573c] StatisticalMeasures v0.1.7
  [c062fc1d] StatisticalMeasuresBase v0.1.2
  [64bff920] StatisticalTraits v3.4.0
  [82ae8749] StatsAPI v1.7.0
  [2913bbd2] StatsBase v0.34.4
  [4c63d2b9] StatsFuns v1.3.2
  [892a3eda] StringManipulation v0.4.1
⌃ [09ab397b] StructArrays v0.6.21
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.12.0
  [3eeacb1d] TabularDisplay v1.3.0
  [10284c91] TaijaBase v1.2.3
  [bd7198b4] TaijaPlotting v1.3.0
  [62fd8b95] TensorCore v0.1.1
  [ac1d9e8a] ThreadsX v0.1.12
  [3bb67fe8] TranscodingStreams v0.11.3
  [28d57a85] Transducers v0.4.84
  [592b5752] Trapz v2.0.3
  [bc48ee85] Tullio v0.3.8
  [5c2747f8] URIs v1.5.1
  [3a884ed6] UnPack v1.0.2
  [1cfade01] UnicodeFun v0.4.1
  [1986cc42] Unitful v1.22.0
  [45397f5d] UnitfulLatexify v1.6.4
  [013be700] UnsafeAtomics v0.3.0
  [41fe7b60] Unzip v0.2.0
⌅ [e88e6eb3] Zygote v0.6.75
  [700de1a5] ZygoteRules v0.2.7
⌅ [68821587] Arpack_jll v3.5.1+1
  [6e34b625] Bzip2_jll v1.0.9+0
  [83423d85] Cairo_jll v1.18.2+1
  [ee1fde0b] Dbus_jll v1.14.10+0
  [5ae413db] EarCut_jll v2.2.4+0
  [2702e6a9] EpollShim_jll v0.0.20230411+1
  [2e619515] Expat_jll v2.6.5+0
⌅ [b22a6f82] FFMPEG_jll v4.4.4+1
  [a3f928ae] Fontconfig_jll v2.15.0+0
  [d7e528f0] FreeType2_jll v2.13.3+1
  [559328eb] FriBidi_jll v1.0.16+0
  [0656b61e] GLFW_jll v3.4.0+2
  [d2c73de3] GR_jll v0.73.13+0
  [78b55507] Gettext_jll v0.21.0+0
  [f8c6e375] Git_jll v2.47.1+0
  [7746bdde] Glib_jll v2.82.4+0
  [3b182d85] Graphite2_jll v1.3.14+1
  [2e76f6c2] HarfBuzz_jll v8.5.0+0
  [aacddb02] JpegTurbo_jll v3.1.1+0
  [c1c5ebd0] LAME_jll v3.100.2+0
  [88015f11] LERC_jll v4.0.1+0
  [dad2f222] LLVMExtra_jll v0.0.35+0
  [1d63c593] LLVMOpenMP_jll v18.1.7+0
  [dd4b983a] LZO_jll v2.10.3+0
⌅ [e9f186c6] Libffi_jll v3.2.2+2
  [d4300ac3] Libgcrypt_jll v1.11.0+0
  [7e76a0d4] Libglvnd_jll v1.7.0+0
  [7add5ba3] Libgpg_error_jll v1.51.1+0
  [94ce4f54] Libiconv_jll v1.18.0+0
  [4b2f31a3] Libmount_jll v2.40.3+0
  [89763e89] Libtiff_jll v4.7.1+0
  [38a345b3] Libuuid_jll v2.40.3+0
⌅ [0e4427ef] LightGBM_jll v3.3.5+1
  [e7412a2a] Ogg_jll v1.3.5+1
  [458c3c95] OpenSSL_jll v3.0.16+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [91d4177d] Opus_jll v1.3.3+0
  [36c8627f] Pango_jll v1.56.1+0
⌅ [30392449] Pixman_jll v0.43.4+0
⌅ [c0090381] Qt6Base_jll v6.7.1+1
⌅ [629bc702] Qt6Declarative_jll v6.7.1+2
⌅ [ce943373] Qt6ShaderTools_jll v6.7.1+1
⌃ [e99dba38] Qt6Wayland_jll v6.7.1+1
  [f50d1b31] Rmath_jll v0.5.1+0
  [a44049a8] Vulkan_Loader_jll v1.3.243+0
  [a2964d1f] Wayland_jll v1.21.0+2
  [2381bf8a] Wayland_protocols_jll v1.36.0+0
  [02c8fc9c] XML2_jll v2.13.6+1
  [aed1982a] XSLT_jll v1.1.42+0
  [ffd25f8a] XZ_jll v5.6.4+1
  [f67eecfb] Xorg_libICE_jll v1.1.1+0
  [c834827a] Xorg_libSM_jll v1.2.4+0
  [4f6342f7] Xorg_libX11_jll v1.8.6+3
  [0c0b7dd1] Xorg_libXau_jll v1.0.12+0
  [935fb764] Xorg_libXcursor_jll v1.2.3+0
  [a3789734] Xorg_libXdmcp_jll v1.1.5+0
  [1082639a] Xorg_libXext_jll v1.3.6+3
  [d091e8ba] Xorg_libXfixes_jll v6.0.0+0
  [a51aa0fd] Xorg_libXi_jll v1.8.2+0
  [d1454406] Xorg_libXinerama_jll v1.1.5+0
  [ec84b674] Xorg_libXrandr_jll v1.5.4+0
  [ea2f1a96] Xorg_libXrender_jll v0.9.11+1
  [14d82f49] Xorg_libpthread_stubs_jll v0.1.2+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.0+3
  [cc61e674] Xorg_libxkbfile_jll v1.1.2+1
  [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.4+0
  [12413925] Xorg_xcb_util_image_jll v0.4.0+1
  [2def613f] Xorg_xcb_util_jll v0.4.0+1
  [975044d2] Xorg_xcb_util_keysyms_jll v0.4.0+1
  [0d47668e] Xorg_xcb_util_renderutil_jll v0.3.9+1
  [c22f9ab0] Xorg_xcb_util_wm_jll v0.4.1+1
  [35661453] Xorg_xkbcomp_jll v1.4.6+1
  [33bec58e] Xorg_xkeyboard_config_jll v2.39.0+0
  [c5fb5394] Xorg_xtrans_jll v1.5.1+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [35ca27e7] eudev_jll v3.2.9+0
  [214eeab7] fzf_jll v0.56.3+0
  [1a1c6b14] gperf_jll v3.1.1+1
  [a4ae2306] libaom_jll v3.11.0+0
  [0ac62f75] libass_jll v0.15.2+0
  [1183f4f0] libdecor_jll v0.2.2+0
  [2db6ffa8] libevdev_jll v1.11.0+0
  [f638f0a6] libfdk_aac_jll v2.0.3+0
  [36db933b] libinput_jll v1.18.0+0
  [b53b4c65] libpng_jll v1.6.46+0
  [f27f6e37] libvorbis_jll v1.3.7+2
  [009596ad] mtdev_jll v1.1.6+0
⌅ [1270edf5] x264_jll v2021.5.5+0
⌅ [dfaa095f] x265_jll v3.5.0+0
  [d8fb68d0] xkbcommon_jll v1.4.1+2
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+4
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
WARNING: Method definition _dummy_backedge() in module Memoization at /home/pkgeval/.julia/packages/Memoization/ON3Za/src/Memoization.jl:49 overwritten at /home/pkgeval/.julia/packages/Memoization/ON3Za/src/Memoization.jl:65.
┌ Warning: Unable to determine HTML(edit_link = ...) from remote HEAD branch, defaulting to "master".
│ Calling `git remote` failed with an exception. Set JULIA_DEBUG=Documenter to see the error.
│ Unless this is due to a configuration error, the relevant variable should be set explicitly.
└ @ Documenter ~/.julia/packages/Documenter/iwb7N/src/utilities/utilities.jl:651
[ Info: SetupBuildDirectory: setting up build directory.
[ Info: Doctest: running doctests.
[ Info: Skipped ExpandTemplates step (doctest only).
[ Info: Skipped CrossReferences step (doctest only).
[ Info: Skipped CheckDocument step (doctest only).
[ Info: Skipped Populate step (doctest only).
[ Info: Skipped RenderDocument step (doctest only).
Test Summary:                 | Pass  Total   Time
Doctests: ConformalPrediction |    1      1  25.7s
WARNING: using deprecated binding ColorTypes.RGB1 in Colors.
, use XRGB instead.
WARNING: using deprecated binding Colors.RGB1 in PlotUtils.
, use ColorTypes.XRGB{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead.
WARNING: PlotUtils.RGB1 is deprecated, use ColorTypes.XRGB{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead.
  likely near /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/aqua.jl:3
WARNING: using deprecated binding ColorTypes.RGB4 in Colors.
, use RGBX instead.
WARNING: using deprecated binding Colors.RGB4 in PlotUtils.
, use ColorTypes.RGBX{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead.
WARNING: PlotUtils.RGB4 is deprecated, use ColorTypes.RGBX{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead.
  likely near /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/aqua.jl:3
[ Info: For silent loading, specify `verbosity=0`. 
import NearestNeighborModels ✔
[ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import EvoTrees ✔
┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123)
└ , …), …).
┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 8016, 7014, 286))
└ , …), …).
┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 15030, 14028, 172))
└ , …), …).
┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 22044, 21042, 158))
└ , …), …).
┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 36072, 35070, 30))
└ , …), …).
┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 49098, 48096, 504))
└ , …), …).
┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 63126, 62124, 76))
└ , …), …).
┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 160320, 159318, 779))
└ , …), …).
┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 167334, 166332, 665))
└ , …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJDecisionTreeInterface ✔
[ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJLinearModels ✔
[ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: For silent loading, specify `verbosity=0`. 
import MLJDecisionTreeInterface ✔
[ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJLinearModels ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(NaiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Training machine(JackknifeMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RidgeRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: For silent loading, specify `verbosity=0`. 
import MLJLinearModels ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(NaiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: Training machine(JackknifeMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LassoRegressor(lambda = 1.0, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …).
┌ Info: Solver: MLJLinearModels.ProxGrad
│   accel: Bool true
│   max_iter: Int64 1000
│   tol: Float64 0.0001
│   max_inner: Int64 100
│   beta: Float64 0.8
└   gram: Bool false
[ Info: For silent loading, specify `verbosity=0`. 
import EvoTrees ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
┌ Info: Training machine(CVPlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123)
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(CVPlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 43086, 42084, 916))
└ , …), …).
┌ Info: Training machine(NaiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 84168, 83166, 435))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(NaiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 91182, 90180, 721))
└ , …), …).
┌ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 98196, 97194, 607))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 233466, 232464, 341))
└ , …), …).
┌ Info: Training machine(JackknifePlusAbRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 356712, 355710, 101))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(JackknifePlusAbRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 490980, 489978, 836))
└ , …), …).
┌ Info: Training machine(JackknifePlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 614226, 613224, 593))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(JackknifePlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 3973932, 3972930, 532))
└ , …), …).
┌ Info: Training machine(CVMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 7173318, 7172316, 789))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(CVMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 7216404, 7215402, 705))
└ , …), …).
┌ Info: Training machine(JackknifeRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 7257486, 7256484, 224))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(JackknifeRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 10624206, 10623204, 452))
└ , …), …).
┌ Info: Training machine(JackknifeMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 13830606, 13829604, 598))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(JackknifeMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 17190312, 17189310, 540))
└ , …), …).
┌ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 20389698, 20388696, 798))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 20551020, 20550018, 480))
└ , …), …).
┌ Info: Training machine(SimpleInductiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 20692302, 20691300, 198))
└ , …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
┌ Info: Training machine(SimpleInductiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: Random.MersenneTwister(123, (0, 20745408, 20744406, 889))
└ , …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import NearestNeighborModels ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(NaiveRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(JackknifeMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = KNNRegressor(K = 5, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = KNNRegressor(K = 5, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJDecisionTreeInterface ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifeMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = DecisionTreeRegressor(max_depth = -1, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJLinearModels ✔
[ Info: Training machine(ConformalQuantileRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(ConformalQuantileRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(CVPlusRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(NaiveRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Training machine(JackknifeMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = QuantileRegressor(delta = 0.5, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = QuantileRegressor(delta = 0.5, …), …), …).
┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
│   optim_options: Optim.Options{Float64, Nothing}
└   lbfgs_options: @NamedTuple{} NamedTuple()
[ Info: For silent loading, specify `verbosity=0`. 
import MLJDecisionTreeInterface ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(NaiveRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(JackknifeMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = RandomForestRegressor(max_depth = -1, …), …), …).
[ Info: For silent loading, specify `verbosity=0`. 
import MLJLinearModels ✔
┌ Warning: This test is skipped as the method is not suitable for Quantile Regression
└ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80
[ Info: Training machine(CVPlusRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVPlusRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(NaiveRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(NaiveRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(JackknifePlusAbRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusAbRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(JackknifePlusRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifePlusRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(CVMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(CVMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(JackknifeRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Training machine(JackknifeMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(JackknifeMinMaxRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LinearRegressor(fit_intercept = true, …), …), …).
[ Info: Training machine(SimpleInductiveRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable.
[ Info: Training machine(SimpleInductiveRegressor(model = LinearRegressor(fit_intercept = true, …), …), …).
┌ Info: Solver: MLJLinearModels.Analytical
│   iterative: Bool false
└   max_inner: Int64 200
[ Info: For silent loading, specify `verbosity=0`. 
import MLJDecisionTreeInterface ✔
Test Summary:          | Pass  Total      Time
ConformalPrediction.jl |  504    504  21m49.4s
     Testing ConformalPrediction tests passed 

Testing completed after 1408.55s

PkgEval succeeded after 2011.74s